Map-image registration accuracy using least-squares polynomials

Abstract

Results of a simulation study of map-image rectification accuracy are reported. Sample size, spatial distribution pattern and measurement errors in a set of ground control points, and the computational algorithm employed to derive the estimate of the parameters of a least-squares bivariate map-image transformation function, are varied in order to assess the sensitivity of the procedure. Standard errors and confidence limits are derived for each of 72 cases, and it is shown that the effects of all four factors are significant. Standard errors fall rapidly as sample size increases, and rise as the control point pattern becomes more linear. Measurement error is shown to have a significant effect on both accuracy and precision. The Gram-Schmidt orthogonal polynomial algorithm performs consistently better than the Gauss-Jordan matrix inversion procedure in all circumstances.